Removal of citrate and hypophosphite binary components using Fenton,photo-Fenton and electro-Fenton processes

Both citrate and hypophosphite in aqueous solution were degraded by advanced oxidation processes （Fe^2＋/H2O2, UV/Fe^2＋/H2O2, and electrolysis/Fe^2＋/H2O2） in this study. Comparison of these techniques in oxidation efficiency was undertaken. It was found that Fenton process could not completely degrade citrate in the presence of hypophosphite since it caused a series inhibition. Therefore, UV light （photo-Fenton） or electron current （electro-Fenton） was applied to improve the degradation efficiency of the Fenton process. Results showed that both photo-Fenton and electro-Fenton processes could overcome the inhibition of hypophosphite, especially the electro-Fenton.

Personal Review:Progress and prospect in electro-Fenton process for wastewater treatment

As a novel advanced oxidation process (AOP), electro-Fenton process is powerful for degrading most organic compounds including toxic and non-biodegradable ones, and so has attracted great attention. This paper reviews this process in detail including the mechanism, electrolytic bath, electrode materials, aerations and operation parameters. The application of electro-Fenton method in wastewater treatment is evaluated and summarized. Future work in this field is suggested, and three main directions of new electrode exploitation, development of assisted technologies and mechanistic study should be strengthened.

电芬顿法(Electro-Fenton Process)处理废水

电芬顿法(electro-Fenton)是在Fenton试剂的基础上,在二价铁离子(Fe2+)和过氧化氢(H2O2)作用下,产生强氧化性的羟基自由基(●OH)并由其氧化难降解废水,其中Fe2+和H2O2分别由电化学过程产生。本文概述了电芬顿过程的基本原理,通过介绍国内外电芬顿的发展情况,详细介绍了电芬顿的影响因素,包括pH、阴极电极、催化剂及其他因素等,并展望了其未来的发展方向。

Kinetic degradation of amoxicillin by using the electro-Fenton process in the presence of a graphite rods from used batteries

This study reports the removal of amoxicillin(AMX)in aqueous media using the electroFenton process in the presence of a graphite cathode recovered from used batteries.The impact of the relevant parameters on the electroFenton process,namely the applied current intensity,the temperature,the initial concentration of AMX and the initial concentration of ferrous ions were investigated.The results showed that the optimal values were:I=600 mA,T=25℃,[AMX]_(0)=0.082 mmol·L^( 1) and[Fe^(2+)]=1 mmol·L^(1),leading to 95%degradation and 74%mineralization.The model parameters of AMX mineralization were determined using nonlinear methods,showing that it follows a pseudosecondorder kinetic.The Energy consumption(EC)calculated under the optimal values was found to be 0.79 kWhg 1,which was of the same order of magnitude of those reported in other findings;while it is noteworthy that the electrodes used in our study are of a lower cost.

Simultaneous utilization of electro-generated O_(2)and H_(2)for H_(2)O_(2)production:An upgrade of the Pd-catalytic electro-Fenton process for pollutants degradation

The Electro-Fenton(EF)process is one of the promising advanced oxidation processes(AOPs)for environmental remediation.The H_(2)O_(2) yield of EF process largely determines its performance on organic pollutants degradation.Conventional Pd-catalytic EF process generates H_(2)O_(2) via the combination reaction of anodic O_(2) and cathodic H;.However,the relatively expensive catalyst limits its application.Herein,a hybrid Pd/activated carbon(Pd/AC)-stainless steel mesh(SS)cathode(PACSS)was proposed,which enables more efficie nt H_(2)O_(2)generation.It utilizes AC,the support of Pd catalyst,as part of cathode for H_(2)O_(2) generation via 2-electron anodic O_(2) reduction,and SS serve as a current distributor.Moreover,H_(2)O_(2) could be catalytically decomposed upon AC to generate highly reactive·OH,which avoids the use of Fe;.Compared with conventional Pd catalyst,H_(2)O_(2) concentration obtained by PACSS cathode is248.2%higher,the O_(2)utilization efficiency was also increased from 3.2%to 10.8%.Within 50 min,26.3%,72.5%,and 94.0%H_(2)O_(2) was decomposed by Pd,AC,and Pd/AC.Fluorescence detection results implied that Pd/AC is effective upon H_(2)O_(2) activation for·OH generation.Finally,iron-free EF process enabled by PACSS cathode was examined to be effective for reactive blue 19(RB19)degradation.After continuous running for 10 cycles(500 min),the PACSS cathode was still stable for H_(2)O_(2)generation,H_(2)O_(2)activation,and RB19 degradation,showing its potential application for organic pollutants degradation without increase in the running cost.

Removal of 17β-Estradiol by Electro-Fenton Process

The Electrochemical advanced oxidation method “Electro-Fenton” has been applied to remove 17β-estradiol (17β)- estra-1,3,5(10)-triéne-3,17-diol) in aqueous-acetonitrile mixture. This endocrine disrupting is a steroid hormone, releases from humans, animals and residual pharmaceuticals into the environmental water and usually causes suspected undesirable effects in aquatic organisms. The degradation of this organic compound by Electro-Fenton process was showed using a carbon felt cathode and platinum anode. The evolution of the concentration during treatment was followed up by high performance liquid chromatography (HPLC). The influence of operating conditions on the degradation of 17β-estradiol by Electro-Fenton step, such as initial concentration and catalyst concentration, has been investi- gated and discussed. We showed that the degradation reaction obeyed apparent first-order reaction kinetics, with absolute rate constant determined as 5.12 × 109 M–1 s–1 by competitive kinetics method taking Benzoic Acid as reference compound. The results confirm the efficiency of the Electro-Fenton process to degrade organic pollutant in aqueous-acetonitrile mixture.

Reinforcement Learning in Process Industries:Review and Perspective

This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries.

NADARAYA-WATSON ESTIMATORS FOR REFLECTED STOCHASTIC PROCESSES

We study the Nadaraya-Watson estimators for the drift function of two-sided reflected stochastic differential equations.The estimates,based on either the continuously observed process or the discretely observed process,are considered.Under certain conditions,we prove the strong consistency and the asymptotic normality of the two estimators.Our method is also suitable for one-sided reflected stochastic differential equations.Simulation results demonstrate that the performance of our estimator is superior to that of the estimator proposed by Cholaquidis et al.(Stat Sin,2021,31:29-51).Several real data sets of the currency exchange rate are used to illustrate our proposed methodology.

Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Correlation of Microbiological Stability with Redox Processes in White Wines

In this paper, the authors analyzed the correlation between the microbiological stability of white wines and the content of sulfur dioxide, which influences the main redox processes that take place in the technological stages of the wine. The consecutive, parallel and spontaneous development of several redox processes and their impact on the quality, microbiological and crystalline stability of white wines were examined. The reduction of additive and subtractive technological interventions, of the amounts of adjuvants (sulphurous anhydride) is essential for the production of organic wines.

基于Arduino与Processing的心率检测计设计研究

随着社会经济的高速发展,人们的物质生活也有了极大的提高,但同时也伴随着各种疾病的到来,身体健康已经成为人们普遍关注的焦点,因此,心率检测仪、血压计、血糖仪等各种家用医疗监测仪器已经逐渐融入日常生活。心脏病是人们难以预防的突发致命疾病之一,本文介绍的是一款基于Arduino【是基于易用硬件和软件的原型开源平台,包由可编程的电路板(简称微控制器),以及集成开发环境(称为Arduino IDE)的现成软件组成】Processing(开源编程语言,包括编辑器、编译器、展示器)的简易心率检测计系统,其功能实用、操作简单,可以测量心率,当超出正常心率范围时及时预警,是一款便携的实时心率测试仪。

Cookie Baking Process Optimization and Quality Analysis Based on Food 3D Printing

In order to obtain better quality cookies, food 3D printing technology was employed to prepare cookies. The texture, color, deformation, moisture content, and temperature of the cookie as evaluation indicators, the influences of baking process parameters, such as baking time, surface heating temperature and bottom heating temperature, on the quality of the cookie were studied to optimize the baking process parameters. The results showed that the baking process parameters had obvious effects on the texture, color, deformation, moisture content, and temperature of the cookie. All of the roasting surface heating temperature, bottom heating temperature and baking time had positive influences on the hardness, crunchiness, crispiness, and the total color difference(ΔE) of the cookie. When the heating temperatures of the surfac and bottom increased, the diameter and thickness deformation rate of the cookie increased. However,with the extension of baking time, the diameter and thickness deformation rate of the cookie first increased and then decreased. With the surface heating temperature of 180 ℃, the bottom heating temperature of 150 ℃, and baking time of 15 min, the cookie was crisp and moderate with moderate deformation and uniform color. There was no burnt phenomenon with the desired quality. Research results provided a theoretical basis for cookie manufactory based on food 3D printing technology.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

THE EXTREMES OF DEPENDENT CHI-PROCESSES ATTRACTED BY THE BROWN-RESNICK PROCESS

Motivated by some recent works on the topic of the Brown-Resnick process, we study the functional limit theorem for normalized pointwise maxima of dependent chi-processes. It is proven that the properly normalized pointwise maxima of those processes are attracted by the Brown-Resnick process.

Investigation on taste characteristics and sensory perception of soft-boiled chicken during oral processing based on electronic tongue and electronic nose

The sensory perception of food is a dynamic process,which is closely related to the release of flavor substances during oral processing.It’s not only affected by the food material,but also subjected to the individual oral environment.To explore the oral processing characteristics of soft-boiled chicken,the sensory properties,texture,particle size,viscosity,characteristic values of electronic nose and tongue of different chicken samples were investigated.The correlation analysis showed that the physical characteristics especially the cohesiveness,springiness,resilience of the sample determined oral processing behavior.The addition of chicken skin played a role in lubrication during oral processing.The particle size of the bolus was heightened at the early stage,and the fluidity was enhanced in the end,which reduced the chewing time to the swallowing point and raised the aromatic compounds signal of electronic nose.But the effect of chicken skin on chicken thigh with relatively high fat content,was opposite in electronic nose,which had a certain masking effect on the perception of umami and sweet taste.In conclusion,fat played a critical role in chicken oral processing and chicken thigh had obvious advantages in comprehensive evaluation of soft-boiled chicken,which was more popular among people.

Modeling and analysis of air combustion and steam regeneration in methanol to olefins processes

Light olefins is the incredibly important materials in chemical industry.Methanol to olefins(MTO),which provides a non-oil route for light olefins production,received considerable attention in the past decades.However,the catalyst deactivation is an inevitable feature in MTO processes,and regeneration,therefore,is one of the key steps in industrial MTO processes.Traditionally the MTO catalyst is regenerated by removing the deposited coke via air combustion,which unavoidably transforms coke into carbon dioxide and reduces the carbon utilization efficiency.Recent study shows that the coke species over MTO catalyst can be regenerated via steam,which can promote the light olefins yield as the deactivated coke species can be essentially transferred to industrially useful synthesis gas,is a promising pathway for further MTO processes development.In this work,we modelled and analyzed these two MTO regeneration methods in terms of carbon utilization efficiency and technology economics.As shown,the steam regeneration could achieve a carbon utilization efficiency of 84.31%,compared to 74.74%for air combustion regeneration.The MTO processes using steam regeneration can essentially achieve the near-zero carbon emission.In addition,light olefins production of the MTO processes using steam regeneration is 12.81%higher than that using air combustion regeneration.In this regard,steam regeneration could be considered as a potential yet promising regeneration method for further MTO processes,showing not only great environmental benefits but also competitive economic performance.

Fresh Processing Technology in Polygonatum odoratum Production Area and Its Comparison with Traditional Processing

[Objectives]To compare the effects of traditional processing and fresh processing on the quality of Polygonatum odoratum decoction piece.[Methods]The effects of fresh processing and traditional processing on the quality of P.odoratum decoction piece were compared and analyzed with appearance characteristics,total ash content,extract content,total polysaccharides content,and total flavonoids content as the evaluation indexes.[Results]Fresh processing method in different production areas has different effects on P.odoratum decoction piece.P.odoratum was dried in oven of 50℃.When moisture content was 41.44%-59.67%,it was cut.After complete drying at 50℃,the moisture content of dried P.odoratum was 8.94%-9.60%,and ethanol-soluble extract content was 77.29%-78.20%,and water-soluble extract was 77.7%-78.14%.At this time,the appearance characteristics of section of P.odoratum decoction piece were better than that of traditional processing,which was yellowish white.The total polysaccharide content was higher than that of traditional processing,and the content of total flavonoids was statistically significant different from that of traditional processing.[Conclusions]The quality of P.odoratum decoction piece by fresh processing is better than that of the traditional processing,and it is feasible to use fresh processing.

Case Retrieval Strategy of Turning Process Based on Grey Relational Analysis

To solve the problem of long response time when users obtain suitable cutting parameters through the Internet based platform,a case-based reasoning framework is proposed.Specifically,a Hamming distance and Euclidean distance combined method is designed to measure the similarity of case features which have both numeric and category properties.In addition,AHP(Analytic Hierarchy Process)and entropy weight method are integrated to provide features weight,where both user preferences and comprehensive impact of the index have been concerned.Grey relation analysis is used to obtain the similarity of a new problem and alternative cases.Finally,a platform is also developed on Visual Studio 2015,and a case study is demonstrated to verify the practicality and efficiency of the proposed method.This method can obtain cutting parameters which is suitable without iterative calculation.Compared with the traditional PSO(Particle swarm optimization algorithm)and GA(Genetic algorithm),it can obtain faster response speed.This method can provide ideas for selecting processing parameters in industrial production.While guaranteeing the characteristic information is similar,this approach can select processing parameters which is the most appropriate for the production process and a lot of time can be saved.